A Hybrid Deterministic Model Based on Rough set and Fuzzy set and Bayesian Optimal Classifier

2006 ◽  
Author(s):  
Hongsheng Su ◽  
Qunzhan Li
Keyword(s):  
Rough Set ◽  
Fuzzy Set ◽  
Deterministic Model ◽  
Model Based ◽  
Optimal Classifier

An Improved Covering Fuzzy Rough Set Model

2012 ◽  
Vol 490-495 ◽  
pp. 1397-1401
Author(s):  
Qing Hai Wang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set ◽  
Rough Set Theory ◽  
Real Life ◽  
Fuzzy Rough Set ◽  
Application Range ◽  
Model Based ◽  
Lower And Upper Approximations ◽  
The Universe

In this paper, we proposed the covering fuzzy rough set model based on multi-granulations and discussed some interesting properties about the model. The research may enlarge the application range of the rough set theory in real life. The lower and upper approximations of fuzzy set are defined by multi-covering relations on the universe, and some basic properties are introduced. It is shown that the fuzzy rough set model based on multi-covering relations is an extension of the rough set model based on multi-granulations.

scholarly journals Multiple Granulation Rough Set Approach to Interval-Valued Intuitionistic Fuzzy Ordered Information Systems

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 949
Author(s):  
Zhen Li ◽  
Xiaoyan Zhang
Keyword(s):  
Information Theory ◽  
Pattern Recognition ◽  
Information System ◽  
Information Systems ◽  
Rough Set ◽  
Fuzzy Set ◽  
Equivalence Relation ◽  
Target Concept ◽  
Actual Case ◽  
Interval Valued

As a further extension of the fuzzy set and the intuitive fuzzy set, the interval-valued intuitive fuzzy set (IIFS) is a more effective tool to deal with uncertain problems. However, the classical rough set is based on the equivalence relation, which do not apply to the IIFS. In this paper, we combine the IIFS with the ordered information system to obtain the interval-valued intuitive fuzzy ordered information system (IIFOIS). On this basis, three types of multiple granulation rough set models based on the dominance relation are established to effectively overcome the limitation mentioned above, which belongs to the interdisciplinary subject of information theory in mathematics and pattern recognition. First, for an IIFOIS, we put forward a multiple granulation rough set (MGRS) model from two completely symmetry positions, which are optimistic and pessimistic, respectively. Furthermore, we discuss the approximation representation and a few essential characteristics for the target concept, besides several significant rough measures about two kinds of MGRS symmetry models are discussed. Furthermore, a more general MGRS model named the generalized MGRS (GMGRS) model is proposed in an IIFOIS, and some important properties and rough measures are also investigated. Finally, the relationships and differences between the single granulation rough set and the three types of MGRS are discussed carefully by comparing the rough measures between them in an IIFOIS. In order to better utilize the theory to realistic problems, an actual case shows the methods of MGRS models in an IIFOIS is given in this paper.

scholarly journals Research on Rough Set Model Based on Golden Ratio

2011 ◽  
Vol 15 ◽  
pp. 1947-1951
Author(s):  
Yu-Ming Zhai ◽  
Rui-Xia Yan ◽  
Hai-Feng Liu
Keyword(s):  
Rough Set ◽  
Golden Ratio ◽  
Model Based

Variable Precision Rough Set Model Based on Covering Relation and Uncertainty Measure

2013 ◽  
Vol 694-697 ◽  
pp. 2856-2859
Author(s):  
Mei Yun Wang ◽  
Chao Wang ◽  
Da Zeng Tian
Keyword(s):  
Rough Set ◽  
Uncertainty Measure ◽  
Probabilistic Measure ◽  
Covering Relation ◽  
Practical Applications ◽  
Variable Precision ◽  
Model Based ◽  
Equivalent Relation ◽  
Proposed Model ◽  
Variable Precision Rough Set

The variable precision probabilistic rough set model is based on equivalent relation and probabilistic measure. However, the requirements of equivalent relation and probabilistic measure are too strict to satisfy in some practical applications. In order to solve the above problem, a variable precision rough set model based on covering relation and uncertainty measure is proposed. Moreover, the upper and lower approximation operators of the proposed model are given, while the properties of the operators are discussed.

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